logdiv 0.0.2.1

A package to mesure diversity of log files

LogDiv: A Python Module for Computing Diversity in Transaction Logs

LogDiv is a Python module for the computation of the diversity of items requested by users in transaction logs.

It takes two inputs:

1. A log file with transactions.
2. A file with item atributes.

Computing the diversity of items requested by users is a task of interest in many fields, such as sociology, recommender systems, e-commerce, and media studies. Check the example below.

Getting Started

Prerequisites

LogDiv requires:

• Python
• Numpy - Essential
• Pandas - Essential
• Matplotlib - Essential
• tqdm - Optionnal: progression bar
• Graph-tool - Optionnal: only one function requires it

$ pip install numpy
$ pip install panda
$ pip install matplotlib 
$ pip install tqdm 

Installing

To install LogDiv, you need to execute:

$ pip install logdiv

Specification

Entries format

LogDiv needs a specific format of entries to run:

• A file describing all requests under a table format, whose fields are:

• user ID
• timestamp
• requested item ID
• referrer item ID

• A file describing all pages visited under a table format, whose fields are:

• item ID
• topic
• category

YAML file

Codes that use LogDiv are directed by a YAML file: if you want to modify entry files, or the features you want to compute, you just need to modify the YAML file, not the code itself. This file is self-explanatory.

Examples

You dispose of two examples to familiarize yourself with LogDiv:

• Example 1 uses a short dataset to show how to use LogDiv
• Example 2 uses a dataset of more than 500 thousands of requests to show what kind of results can be obtained

These examples (dataset, script and yaml file) can be found in datasets directory.

Example 1

The following example illustrates the entries format of the package.

user	timestamp	requested_item	referrer_item
user1	2019-07-03 00:00:00	v1	v4
user1	2019-07-03 00:01:00	v4	v2
user1	2019-07-03 00:01:10	v4	v6
user1	2019-07-03 00:01:20	v4	v6
user1	2019-07-03 00:02:00	v6	v9
user1	2019-07-03 03:00:00	v8	v10
user1	2019-07-03 03:01:00	v8	v5
user2	2019-07-05 12:00:00	v3	v5
user2	2019-07-05 12:00:30	v5	v7
user2	2019-07-05 12:00:45	v7	v9
user2	2019-07-05 12:01:00	v9	v6
user3	2019-07-05 18:00:00	v10	v5
user3	2019-07-05 18:01:15	v10	v7
user3	2019-07-05 18:03:35	v10	v9
user3	2019-07-05 18:06:00	v7	v4
user3	2019-07-05 18:07:22	v5	v2

item	class1	class2	class3
v1	Football	beginner	France
v2	Tennis	pro	England
v3	Football	beginner	World
v4	Tennis	advanced	France
v5	Rugby	medium	Italy
v6	Football	beginner	England
v7	Tennis	pro	Spain
v8	Football	beginner	France
v9	Tennis	advanced	World
v10	Rugby	medium	World

Example 2